How to detect LEGO bricks by using a deep learning approach?
So I am assuming that you are trying to detect a lego brick from the image. One idea is that you can use transfer learning. Leveraging a pre-trained machine learning model is called transfer learning. The underlying idea behind transfer learning is that one takes a well-trained model from one dataset or domain, and applies it to a new one.  François Chollet has written a very comprehensive guide to transfer learning (https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)

I admit I took some of this information from Christopher Bonnett's article named Classifying e-commerce products based on images and text.

I also suggest using the Lego brick dataset from Kaggle on this link: https://www.kaggle.com/joosthazelzet/lego-brick-images
It has over 12,700 lego brick images.

If processing power is a problem, you can use Amazon Web Services for cloud computing. It is inexpensive for small scale operations like this.

Of course for the object detection, you can always increase the number of convolution layers. However, if you have too many layers, you should also include residual blocks/residual network. This would allow neural networks even with over a thousand layers to operate effectively. This video should help you understand how residual networks work (https://www.youtube.com/watch?v=ahkBkIGdnWQ)

Finally, make sure not to overfit during your training and if you do follow the residual network idea, you should also include upsampling in your convolution neural network( More in here: https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0)

I hope this helped and good luck on your endeavor.